{"total":26,"rows":[{"id":"2402.13178","title":"Benchmarking Retrieval-Augmented Generation for Medicine","created":"2024-02-20","cited_by_count":331,"area":"rag"},{"id":"2504.12330","title":"HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation","created":"2025-04-13","cited_by_count":31,"area":"rag"},{"id":"2408.09199","title":"TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems","created":"2024-08-17","cited_by_count":28,"area":"rag"},{"id":"2410.02721","title":"Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization","created":"2024-10-03","cited_by_count":15,"area":"rag"},{"id":"2602.23368","title":"Keyword search is all you need: Achieving RAG-Level Performance without vector databases using agentic tool use","created":"2025-12-19","cited_by_count":10,"area":"rag"},{"id":"2501.15245","title":"ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval","created":"2025-01-25","cited_by_count":9,"area":"rag"},{"id":"2512.25052","title":"AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG","created":"2025-12-31","cited_by_count":8,"area":"rag"},{"id":"2402.01767","title":"HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA","created":"2024-02-01","cited_by_count":6,"area":"rag"},{"id":"2505.11995","title":"Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation","created":"2025-05-17","cited_by_count":4,"area":"rag"},{"id":"2507.22917","title":"Reading Between the Timelines: RAG for Answering Diachronic Questions","created":"2025-07-21","cited_by_count":4,"area":"rag"},{"id":"2601.16503","title":"MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicine","created":"2026-01-23","cited_by_count":4,"area":"rag"},{"id":"2608.07370","title":"LitTraceQA: A Benchmark for Multi-Stage Grounding and Verification in Scientific Question Answering","created":"2026-08-07","cited_by_count":4,"area":"rag"},{"id":"2406.07257","title":"Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway","created":"2024-06-11","cited_by_count":2,"area":"rag"},{"id":"2508.05660","title":"Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review","created":"2025-07-30","cited_by_count":2,"area":"rag"},{"id":"2601.16478","title":"DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answering","created":"2026-01-23","cited_by_count":2,"area":"rag"},{"id":"2406.13840","title":"StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation","created":"2024-06-19","cited_by_count":1,"area":"rag"},{"id":"2602.03368","title":"Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain","created":"2026-02-03","cited_by_count":1,"area":"rag"},{"id":"2504.12342","title":"CRAB: A Benchmark for Evaluating Curation of Retrieval-Augmented LLMs in Biomedicine","created":"2025-04-15","cited_by_count":0,"area":"rag"},{"id":"2504.14941","title":"WindVE: Collaborative CPU-NPU Vector Embedding","created":"2025-04-21","cited_by_count":0,"area":"rag"},{"id":"2508.06204","title":"Classification is a RAG problem: A case study on hate speech detection","created":"2025-08-08","cited_by_count":0,"area":"rag"},{"id":"2508.20867","title":"MSRS: Evaluating Multi-Source Retrieval-Augmented Generation","created":"2025-08-28","cited_by_count":0,"area":"rag"},{"id":"2509.03891","title":"MobileRAG: Enhancing Mobile Agent with Retrieval-Augmented Generation","created":"2025-09-04","cited_by_count":0,"area":"rag"},{"id":"2509.10469","title":"Real-Time RAG for the Identification of Supply Chain Vulnerabilities","created":"2025-08-23","cited_by_count":0,"area":"rag"},{"id":"2511.03261","title":"Comparing the Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literature","created":"2025-11-05","cited_by_count":0,"area":"rag"},{"id":"2605.02491","title":"From Experimental Limits to Physical Insight: A Retrieval-Augmented Multi-Agent Framework for Interpreting Searches Beyond the Standard Model","created":"2026-05-04","cited_by_count":0,"area":"rag"},{"id":"2607.23006","title":"VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy","created":"2026-07-25","cited_by_count":0,"area":"rag"}],"next_cursor":null}